In general, a reasoning system for estimating output data to be actually detected from an identification target by using a reasoning engine is called a soft sensor because it estimates a physical quantity by arithmetic processing instead of actually detecting a physical quantity like a hard sensor.
As an application of this soft sensor, for example, a system for monitoring the amounts of emissions such as NOx and SOx contained in an exhaust gas from a combustion apparatus by using a neural network is disclosed (see, e.g., JP 9-504346). This system estimates the amounts of emissions such as NOx and SOx discharged from a combustion apparatus by using a neural network using various physical quantities detected by hard sensors as input variables, thereby monitoring the amounts of emissions contained in an exhaust gas instead of using hard sensors for directly and physically detecting these emissions or adjusting these sensors.
In such a conventional soft sensor, however, since a target is identified by using a neural network, the following problems arise when the soft sensor is actually used. First of all, since the neural network involves the model restructuring problem due to its model structure, a relatively long period of time is required for learning. This makes it impossible to follow up changes in targets in real time. It is therefore difficult to meet the expectation that the soft sensor be used instead of a hard sensor for measuring a target exhibiting large changes, e.g., NOx.
In addition, in the case of a neural network, although a statistical evaluation value (average error) can be given to estimated output data, when a soft sensor is actually used, an appropriate evaluation error value cannot be given to estimated output data because of the handling of new input data. Furthermore, when the soft sensor is put into practice, an error in estimated output data cannot be evaluated in real time, and whether the soft sensor is properly functioning cannot be evaluated.
The following problems arise when a target model is created. First of all, in the case of a neural network, as in linear or nonlinear regression model, an input/output relationship must be established throughout the entire input and output spaces from history data according to a predetermined algorithm. However, most of the history data obtained by actual measurement are records of specific states of identification targets. In many cases, therefore, a function shape in the entire input and output spaces is unknown, and hence the consistency between a target and the model created from prepared history data cannot be evaluated. Moreover, when estimation fails, it cannot be determined whether the cause of the failure lies in the shortage of history data used for the creation of the model or a defect in the model design such as the selection of input variables and hierarchical structure.